Matching Graduate Applicants with Faculty Members

  • Shibamouli Lahiri
  • Carmen Banea
  • Rada Mihalcea
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10539)


Every year, millions of students apply to universities for admission to graduate programs (Master’s and Ph.D.). The applications are individually evaluated and forwarded to appropriate faculty members. Considering human subjectivity and processing latency, this is a highly tedious and time-consuming job that has to be performed every year. In this paper, we propose several information retrieval models aimed at partially or fully automating the task. Applicants are represented by their statements of purpose (SOP), and faculty members are represented by the papers they authored. We extract keywords from papers and SOPs using a state-of-the-art keyword extractor. A detailed exploratory analysis of keywords yields several insights into the contents of SOPs and papers. We report results on several information retrieval models employing keywords and bag-of-words content modeling, with the former offering significantly better results. While we are able to correctly retrieve research areas for a given statement of purpose (F-score of 57.7% at rank 2 and 61.8% at rank 3), the task of matching applicants and faculty members is more difficult, and we are able to achieve an F-measure of 21% at rank 2 and 24% at rank 3, when making a selection among 73 faculty members.


Graduate application Statement of purpose Keyword extraction Information retrieval 



We are grateful to Lauren Molley and Joseph Zimmer for their help with the implementation of some system components. We also want to thank the reviewers for their valuable feedback. This material is based in part upon work supported by the Michigan Institute for Data Science, by the National Science Foundation (grant #1344257), and by the John Templeton Foundation (grant #48503). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author and do not necessarily reflect the views of the Michigan Institute for Data Science, the National Science Foundation, or the John Templeton Foundation.


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Shibamouli Lahiri
    • 1
  • Carmen Banea
    • 1
  • Rada Mihalcea
    • 1
  1. 1.University of MichiganAnn ArborUSA

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